class_docstr: Principal Component Analysis (PCA).
  
  Parameters
  ----------
  rescale_with_mean
      Whether or not to subtract each column's mean before performing SVD.
  rescale_with_std
      Whether or not to standardize each column before performing SVD.
  n_components
      The number of principal components to compute.
  n_iter
      The number of iterations used for computing the SVD.
  copy
      Whether nor to perform the computations inplace.
  check_input
      Whether to check the coherence of the inputs or not.
docstr: Transforms row projections back to their original space.
  
  In other words, return a dataset whose transform would be X.
docstr_tok: ["Transforms","row","projections","back","to","their","original","space",".","In","other","words",",","return","a","dataset","whose","transform","would","be","X","."]
code: def inverse_transform(self, X, as_array=False):
      """Transforms row projections back to their original space.
  
      In other words, return a dataset whose transform would be X.
  
      """
  
      X_inv = np.dot(X, self.svd_.V)
  
      if hasattr(self, "scaler_"):
          X_inv = self.scaler_.inverse_transform(X_inv)
  
      if as_array:
          return X_inv
  
      # Extract index
      index = X.index if isinstance(X, pd.DataFrame) else None
      return pd.DataFrame(data=X_inv, index=index)
  
imports: from __future__ import annotations
  import functools
  import altair
  import numpy
  import pandas
  import sklearn.base
  import sklearn.utils
  from sklearn import preprocessing
  from prince import svd, utils
label_desc: Use image node_id 9 for calling the PCA obj's underlying member method code with example usage: obj.inverse_transform(X, as_array) and returns: pd, X_inv